Method in connection with the production of pulp, paper or paperboard

ABSTRACT

Method for predicting properties of a cellulose-fibre-based product, a suspension of cellulose fibre being analysed during manufacture of the product by means of spectroscopic measurements ( 13, 15 ) in a selected spectrum in the wavelength range 200-25000 nm. According to the invention, each sample quantity is diluted ( 3, 4 ) prior to said analysis, following which one partial flow ( 6 ) is dewatered and dried ( 8 ) and used for said spectroscopic measurements ( 13, 15 ), while a second partial flow ( 5 ) of the diluted sample quantity is used for analysis ( 7, 14 ) of physical fibre data, by means of image analysis, each sample quantity analysed generating at least 100 data points from the spectroscopic measurement, and at least 50 data points with regard to said physical fibre data, in the form of one or more physical fibre property distributions, which data points are combined in multivariate data processing ( 16 ), for said prediction.

TECHNICAL FIELD

[0001] The present invention relates to a method for predicting theproperties of a product that consists of cellulose-fibre-based pulp,paper or paperboard, a suspension of cellulose fibre being analysedduring manufacture of the product by means of spectroscopic measurementsof a sample quantity of said suspension in a selected spectrum in thewavelength range 200-25000 nm. According to the invention, physicalfibre data are also analysed for the suspension. Multivariate methodsare used to analyse the combination of measuring data obtained. Theinvention can be applied to both chemical and mechanical pulps.

PRIOR ART

[0002] The commercial production of paper and paperboard often takesplace in at least two mill units separated from one another, the paperpulp producer manufacturing the paper pulp in a first mill anddelivering the same in a dried state to the paper or paperboardmanufacturer, who produces the paper or paperboard in a second mill.Even in integrated mills, i.e. mills where both pulp and paper orpaperboard are produced, the division between the two different parts isclear both as regards the position of the physical installations andtheir operation. The properties of the pulp produced can thus be said toconstitute a “half-time” result, representing the control objective forthe pulp producer and the starting point for the paper or paperboardmanufacturer.

[0003] Producers of paper pulp are confronted today with very highdemands for the pulp that they supply to be of a high, consistent andwell-documented quality. Since the raw material for the pulp producedvaries depending on the type of tree, its locality, the age of the tree,the storage time, the proportion of summer wood/spring wood and theproportion of round timber/sawmill chips, the properties of the pulpwill vary with a starting point in these variations. The properties ofthe pulp are also influenced by variations in the manufacturing process,e.g. varying chip quality, cooking conditions, washing results, changesin the bleaching stages, variations in presses and mixers, and otherplanned or unplanned production changes.

[0004] To be able to offer the customers, i.e. the paper and paperboardmanufacturers, pulp that precisely matches their requirementsspecifications, which specifications differ between different paper orpaperboard manufacturers depending on the product that they manufacture,it is necessary to be able to measure and predict the properties thatreflect the product quality that the respective customer wishes toproduce. Starting out from measured or predicted properties of thiskind, it should be possible to direct each individual base paper bale orroll produced to precisely the customer to which it is suited. Toachieve this, it is of the highest importance to be able to generaterelevant measuring data that is highly reliable. Since pulp productionis continuous, it is also desirable for measuring data to be generatedcontinuously. The continuous generation of measuring data should alsooffer the opportunity of seeing the effect of changes in the rawmaterial and process and of thereby steering the process towards thedesired pulp quality.

[0005] Furthermore, it is desirable for the pulp producer to be able tospecify at the time that he delivers the pulp how this pulp inparticular should be beaten for the desired product attributes to beachieved. For the pulp producer, it would therefore be extremelyvaluable to have an analysis tool that shows the potential of a givenpulp, depending on how it is beaten in a subsequent stage, i.e.depending on the energy input that is used in subsequent beating.

[0006] The methods used commercially today for classifying the pulpproduced are not adequate either with regard to continuity orreliability. Nor is it possible to relate the measured attributes of thepulp to the attributes of the paper or paperboard produced, depending onhow the pulp is beaten. The quality of the finished pulp is judged todayby measuring parameters such as brightness, purity and viscosity with acertain regularity on a selection of production. Customer specificationsbased on the parameters that describe the strength of the pulp are onlymeasured as random samples, since these analyses are bothstaff-intensive and time-consuming. The possibility of recording changesbetween different process stages is often limited to measurements of thekappa number and viscosity. A problem is also constituted by the factthat the properties of the pulp produced vary over a 24-hour period, ashas been shown, and that the parameters that are measured mostfrequently, viscosity, purity and brightness, more often than not arenot relevant measurements of the strength of the pulp.

[0007] In a number of articles from 1989 onwards, it has been shown thatspectroscopy in the near infrared range (NIR) can be used, separately ortogether with multivariate analysis (MVA), to determine the chemical andphysical parameters of paper pulp:

[0008] Birkett, M. D. and Gambino M. J. T., Estimation of pulp kappanumber with near-infrared spectroscopy, Tappi J. September 193-197,(1989);

[0009] Easty D. B., et al., Near-infrared spectroscopy for the analysisof wood pulp: quantifying hardwood-softwood mixtures and estimatinglignin content, Tappi J. 73 (10), 257-261, (1990);

[0010] Wallbäcks, L., Edlund, U and Norden, B., Multivariatecharacterization of pulp, part 1, Spectroscopic characterization ofphysical and chemical differences between pulps using 13C CP/MAS NMR,FT-IR, NIR and multivariate data analysis, Nordic Pulp and PaperResearch Journal no. 2, 74-80, (1991);

[0011] Wallbäcks, L., Edlund, U and Norden, B., Multivariatecharacterization of pulp, part 2, Interpretation and prediction ofbeating processes, Nordic Pulp and Paper Research Journal no. 3,104-109, (1991);

[0012] Wallbäcks, L., Characterization of chemical pulps usingspectroscopy, fibre dimensions and multivariate data analysis,Proceedings 7^(th) international symposium on wood and pulpingchemistry, 802-809, (1993);

[0013] Wallbäcks, L., Edlund, U and Lindgren, T. Multivariatecharacterization of pulp, part 3, Evaluation of physical andspectroscopic data on unbeaten and beaten kraft pulp samples, NordicPulp and Paper Research Journal no. 2, 88-93, (1995);

[0014] Olsson, R. J. O., Tomani P., Karlsson M., Josefsson T., SjöbergK. and Björklund C.: Multivartate characterization of chemical andphysical descriptors in pulp using NIRR, Tappi J., Vol. 78, No. 10(1995);

[0015] Antti, H and Sjöström, M., Multivariate calibration models usingNIR spectroscopy on pulp and paper industrial applications, Journal ofChemometrics, Vol. 10, 591-603, (1996);

[0016] Marklund, A., et al., Prediction of strength parameters forsoftwood kraft pulps. Multivariate data analysis based on orthogonalsignal correction and near infrared spectroscopy, Nordic Pulp and PaperResearch Journal Vol. 14 no. 2, 140-148 (1999);

[0017] Liljenberg, T., et al., On-line NIR characterization of pulp,10^(th) International Symposium on Wood and Pulping Chemistry, 266-269(1999).

[0018] It is also known that physical fibre properties, which aremeasured using different types of fibre meters, are of greatsignificance for the strength attributes of the pulp. The possibility ofusing several length classes and processing these using MVA has beendemonstrated by Marklund, A., et al., Prediction of strength parametersfor softwood kraft pulps. Multivariate data analysis based on physicaland morphological parameters, Nordic Pulp and Paper Research JournalVol. 13 no. 3, 211-219 (1998).

[0019] There are also some patent applications that relate to the fieldof NIR linked to paper pulp; WO 93/05384, WO 95/31710 and WO 95/31709.However, none of these link together the use of NIR data and physicalfibre data with MVA. In other cases, according to the aforementionedreferences, where it is indicated that fibre data such as fibre lengthcan be used together with NIR data, the form in which the fibre data isused is not specified.

[0020] It is in this regard traditionally the case that only mean valuesor rough classifications, and not entire distributions as regards fibredata, have been able to be analysed on-line or at-line.

[0021] In WO97/38305, WO98/28486, WO98/28487, WO98/28488 and WO98/28490,Siemens AG described the control and optimization of a process for themanufacture of paper pulp or paper starting out from multivariateanalysis of input data in the form of spectra of electromagneticradiation and/or mechanical properties. In the applications, the use ofmechanical properties of the fibres is exemplified by a diagram thatshows a distribution curve in which the fibres have been divided up intotwelve different fibre length classes, i.e. a relatively roughclassification.

[0022] In a diploma paper, Bergström M., Multivariate characterizationof mill beaten pulps, using NIR, PQM 1000 and FiberMaster, 1999, UmeåUniversity, the possibility was discussed of combining NIR data andfibre distribution curves with multivariate analysis in order to be ableto predict better the properties of the product. In this case, however,the measurements were carried out on beaten pulp and only so-called NIRscore vectors were used as input data, i.e. not entire NIR spectra.

ACCOUNT OF THE INVENTION

[0023] The object of the present invention is to present a method ofpredicting the properties of a product which consists ofcellulose-fibre-based pulp, paper or paperboard. The method according tothe invention is a further development of the above-named techniques andgives relevant prediction results with a very high level of reliability,thanks to the fact that a very large number of data points from bothspectroscopic measurements and physical fibre analyses (fibredistributions) is used in combination with multivariate analysis.Furthermore, the measurements are carried out continuously, at-line oron-line, at intervals that permit each base paper bale or roll producedto be characterized individually. This gives a unique opportunity todirect the individual bales or rolls to a customer to whom they aresuited.

[0024] These and other objects are achieved by means of the methodaccording to the invention, as presented in claim 1.

[0025] In a specially preferred embodiment of the invention, a samplequantity is extracted on-line at close intervals in time, at a positionin the process line for pulp production that is disposed in advance ofbeating of the pulp. The sample quantity is preferably extracteddirectly prior to a drying stage for the pulp, which drying stage isdisposed directly prior to sale of the dried pulp or directly precedingbeating of the pulp. The analyses according to the invention are thuscarried out preferably on the unbeaten pulp. However, a calibrationmodel is created for prediction that comprises calibrations against pulpsamples that have been beaten with a number of different energy inputs.A possibility is thus obtained by means of the method according to theinvention of predicting potential product properties for a given pulp,depending on how this is beaten in a subsequent stage. The productproperties predicted for an individual base paper bale or roll producedin pulp manufacture can thereby be compared with the desired productproperties for a number of different paper- or paperboard-manufacturingcustomers, it being possible to take this comparison as a basis for thechoice of customer to whom the bale or roll is delivered. From thepredicted properties of the pulp the customer can then advantageouslychoose how the continued beating strategy shall appear for the desiredproduct attributes to be achieved.

[0026] According to one aspect of the invention, the spectrum withinwhich the NIR measurements are carried out consists of the wavelengthrange 400-2500 nm, preferably 780-2500 nm. Within this spectrum, atleast 100 data points, preferably at least 300 data points, even morepreferredly at least 500 data points and most preferredly at least 800data points are generated by means of transmission or reflectance foreach sample quantity extracted. These data points also preferablyconsist of mean values from a number of sweeps in the spectrum,preferably at least 10 sweeps.

[0027] According to another aspect of the invention, said physical fibredata is obtained by means of CCD camera and image analysis, preferablyby means of an apparatus called the STFI FiberMaster, around 10000 fibreimages being analysed for each sample quantity and put together to format least 50 data points, preferably at least 100 data points and evenmore preferredly at least 150 data points. These data points consistaccording to the invention of distribution data and mean values withregard to at least one, preferably at least two and even morepreferredly all physical properties in the group that consists of fibrelength, fibre width and fibre shape, and preferably also of fibre weightper metre and fibre flexibility.

[0028] One problem in connection with combined at-line or on-line fibreanalysis and NIR is that the fibre analysis takes a relatively longtime, since up to 10000 individual fibres are to be photographed foreach sample. This also means that the sample has to be very diluted forthe individual fibres to be visible. For NIR, on the other hand, it isthe case that the analysis is very quick, but that the sample shouldhave a relatively high dry content, often at least 50% depending on themeasuring position. According to one aspect of the invention, theseapparently incompatible requirements are satisfied by a sample quantityextracted being diluted while being agitated to a concentration of lessthan 0.5%, preferably less than 0.1%. A partial flow of the samplequantity is then used for analysing the physical fibre data, and anotherpartial flow is dewatered and dried to at least 50% dry content,preferably at least 70% dry content, and used for the spectroscopicmeasurements. The dewatering and drying is best carried out by filteringcombined with forced air drying, preferably by means of direct contactwith compressed air. The drying takes a little time, but the twoanalyses will nevertheless be in phase, since the fibre analysis alsotakes some time.

[0029] According to one aspect of the invention, the multivariateanalysis is carried out using Principal Component Analysis (PCA),Principal Component Regression (PCR), Partial Least Squares Regression(PLS) or Multiple Linear Regression (MLR). The principle of multivariateanalysis (NVA) utilizing PCA is that a multidimensional data quantitywith variables correlating to one another is projected to a smaller dataquantity with non-correlating variables containing relevant informationat the same time as back noise is eliminated. In general, 2-10 principalcomponents can account for 98% of the variance in the data quantity. PLSis a regression method which uses the information from y-data in aprincipal component distribution of x-data and relates this to theproperty y sought. PLS or one of the other methods named above is usedto set up the calibration model.

[0030] The properties that are predicted according to the invention canbe one or more of the properties that are included in the groupdewatering resistance, density, tensile index, rupture working index,burst index, tear index, tensile rigidity index, surface coarseness,beating requirement, opacity, toughness, light scattering, zero span,air permeance, air resistance, carbohydrate composition, charged groups,fibre type distribution, kappa number, lignin, hexenuronic acid,gravity, brightness, moisture content, viscosity, runnability or other.To determine the calibration model, these properties are thus analysedusing traditional laboratory analysis for a number of samples, followingwhich calibration is carried out between these laboratory results andthe results of the multivariate analysis, so that a calibration model isobtained that is used for predicting the above-named properties infuture at-line or on-line measurements. The calibration model is used toestimate the parameters which are regarded as relevant for therespective customer. In calibration, a distribution of samples is soughtthat is representative of the process and thereby covers the variationthat can be found in the pulp. The calibration model is verified usingan internal validation model (cross-validation), which gives a measureof the model's reliability, e.g. by means of a so-called Q2 value, andsamples analysed using an independent set (external validation), ameasure being obtained of how well the model functions in practice.

[0031] The principle of how MVA is carried out is well known, e.g. fromthe aforementioned references that describe the prior art, and will nottherefore be described in greater detail here. For optimum predictionwith the use of MVA, some supplementary data processing can be carriedout, for example weighting of variables and/or processing of spectralraw data using Orthogonal Signal Correction (OSC), MultiplicativeScatter Correction (MSC), Standard Normal Variate transformation (SNV)or by using a derivator.

BRIEF DESCRIPTION OF DRAWING

[0032] The application of the method according to the invention in themill will be described below and examples will be given with referenceto the figures, of which:

[0033]FIG. 1 shows an elementary diagram of a preferred apparatus set-upfor sampling on-line in a pulp mill,

[0034]FIG. 2 shows a dewatering vessel in the apparatus set-up accordingto FIG. 1, seen from above,

[0035]FIG. 3 shows an example of a so-called Score plot,

[0036]FIG. 4 shows an example of a measured NIR spectrum that is used inthe invention,

[0037]FIGS. 5a-c show an example of measured fibre propertydistributions that are used in the invention,

[0038]FIGS. 6a-c show calibration models for determining the tensileindex measured at 1000 PFI revolutions, starting out from NIR data,fibre data or a combination thereof,

[0039]FIGS. 7a-c show the results of prediction from the calibrationmodels in FIGS. 6a-c,

[0040]FIGS. 8a-c show calibration models for determining the beatingrequirement to achieve the desired tensile index, starting out from NIRdata, fibre data or a combination thereof,

[0041]FIGS. 9a-c show the prediction results from the calibration modelsin FIGS. 8a-c.

[0042] In a pulp mill, e.g. a mill for manufacturing bleached chemicalpulp, a sample quantity is extracted on-line, i.e. a sample flow isextracted by one or more sampling devices 1,1′,1 ^(n) and taken awayfrom the production line via lines 2,2′,2 ^(n) to be analysed inparallel with the same (FIG. 1). Sampling takes place continuously, atleast once every two hours, preferably at least once per hour, even morepreferredly at least twice per hour and most preferredly of all at leastfour times per hour. In certain cases, however, it is conceivable forsampling to take place at longer intervals. The position for samplingcan be anywhere in the pulp line, preferably at the end of the pulpline, prior to beating. Moreover, beating does not normally take placein the pulp line, but in the subsequent line for paper or paperboardproduction.

[0043] The sample quantity extracted is pumped through the lines 2,2′,2^(n) down into a vessel 3 for dilution of the same. Water is added viathe line 4 during simultaneous agitation of the vessel 3, so that thesample in the vessel attains a concentration of a few hundredths of apercent. The sample is then divided into a first partial flow 5 and asecond partial flow 6. The first partial flow 5, which can be less thanthe second partial flow 6, is taken away to equipment 7 for fibreanalysis using a CCD camera in a fibre meter, including computer 14. Thesecond partial flow 6 is taken to a dewatering vessel 8 for dewateringand drying.

[0044]FIG. 2 shows the dewatering vessel 8, seen from above. Thedewatering vessel 8 consists of an open box with a filter 9 arranged inits lower part. An outlet (not shown) for the filtrate is arranged underthe filter 9, which outlet is preferably connected via a vessel (notshown) to a vacuum pump (not shown) preferably controlled by compressedair. Thanks to the filter and vacuum pump, the sample attains a drycontent of around 20%. To attain the desired dry contents for the NIRanalysis, a forced drying stage takes place, which preferably utilizescompressed air 10. A ring line 11 is provided here for compressed air,above the filter 9. The ring line 11 has a quantity of outlet openings12 around the same, which are directed inwards and downwards towards thecentre of the vessel 8. When compressed air is blown out through theseoutlet openings 12, the pulp cake on the filter 9 will be dried to a drycontent exceeding 50%. It is perceived that the dewatering vessel 8,even if shown in the figure as a square box, can equally consist of avessel with a round cross-section, e.g. a cylindrical vessel, the ringline also best following a circular shape.

[0045] When drying has been completed, a measuring head 13 for NIR islowered down towards the filter cake and the NIR measurements arecarried out in the spectrum selected. In the example shown, themeasurements are performed with reflectance using an instrument thatmeasures in the wavelength range 1100-2300 nm and collects the data in acomputer 14, which computer also carries out the MVA analysis. Theresult of the MVA analysis is then used, and also that in the computer,for predicting the expected attributes of paper or paperboardmanufactured from the pulp, from the calibration model set up earlier,with different energy inputs for beating.

[0046] As an example of the invention, FIG. 3 shows an image in whichthe results of a large number of continuous measurements have beencollected in a so-called score plot, where each point describes asample. Since the position describes a certain type of properties, adirect classification of the pulp analysed is obtained. The pulp whichends up in the square furthest to the right has an attribute profilethat is suited to customer A, while the pulp in the square down to theleft meets the requirements set for the pulp for customer B.

EXAMPLE

[0047] Elaboration of Calibration Model—Example from Off-Line Study

[0048] NIR spectroscopy and fibre analysis are used together withmultivariate data analysis (PLS) to determine the tensile index andbeating requirement of fully bleached sulphate pulps.

[0049] Sample

[0050] The reference sample consisted of 29 fully bleached pulp sheetsthat were removed following the drying machine at a chemical sulphatepulp plant. The sample material was divided up so that 16 samples wereused to form a calibration set, while the remaining 13 samples were usedto form an off-line test set. Measurements using NIR spectroscopy,measurements with a fibre analyser and traditional assessments of pulpproperties were carried out on all samples.

[0051] NIR Measurements

[0052] NIR measurements were carried out using an NIRSystems 6500instrument that is equipped with a transport module and a test cell witha surface of approx. 60 cm². The measurements were performed withreflectance directly on the unbeaten dry pulp sheets. The measuringrange was 400-2500 nm with a resolution of 2 nm and each spectrum was amean value of 32 sweeps. When using the entire measuring range, 1050data points are obtained per sample. In the present example, the rangebetween 1200-2500 nm was used, which generates 650 data points persample. An NIR spectrum for a pulp sample is shown in FIG. 4.

[0053] Fibre Measurements

[0054] The fibre measurements were performed on a 0.05% pulp suspensionof the unbeaten samples by means of the STFI FiberMaster. The instrumentmeasures the fibre length, width, shape, flexibility and fibre weightper metre on 10000 fibres by using CCD cameras and image analysis, andsupplies data in the form of distribution curves and mean values whichtogether provide over 170 data points. The distribution curves for fibrelength, width, and shape are shown in FIGS. 5a-c.

[0055] Determining the Sheet Properties

[0056] Assessments of the quality of the pulp samples were performedaccording to standardized methods on laboratory sheets produced frompulp beaten at different speeds using a PFI beater. At each speed, thedewatering resistance, density, tensile index, tear index, tensilerigidity index and air permeance were measured amongst other things. Thebeating requirement to achieve a certain attribute level was determinedby interpolations.

[0057] The present example shows the results for prediction of thetensile index measured at 1000 PFI revolutions and the beatingrequirement to achieve a tensile index of 70 Nm/g. The tensile index isan important strength parameter and is defined as the force required toachieve a break in a paper strip that is clamped in a tensile tester.

[0058] The spectral data matrix from the NIR measurements wastransformed in the example to another derivative to compensate forbaseline drift Linearization of spectral data can also take place usingMultiplicative Scatter Correction (MSC), Orthogonal Signal Correction(OSC) or Standard Normal Variate transformation (SNV).

[0059] Calibration models based on just NIR data, just fibre data andcombinations of these were created by means of PLS in the software SIMCAP from UMETRICS. 650 data points were used from each NIR spectrum and160 data points per sample were used from the FiberMaster measurements.The number of significant components was determined by cross-validation.The calibration model was tested using the off-line test set.

[0060]FIGS. 6a-c show the calibration model for determining the tensileindex measured at 1000 PFI revolutions. In the figure, the analysedvalues for the tensile index are plotted against the predicted values.FIG. 6a is based on purely NIR data, FIG. 6b is based on purelyFiberMaster data and FIG. 6c is based on the combination of these. FIGS.7a-c show the prediction results for the off-line test set for therespective calibration model according to FIGS. 6a-c.

[0061] The models are summarised in Table 1. Both the calibration modelsand the prediction results were improved when using a combination of NIRand fibre analysis. Both R2Y, explained variance, and Q2, predictedvariance, which must both be close to 1, increase. The model error,RMSEE (root mean squared error of estimation) and the prediction errorRMSEP (root mean squared error of prediction) decrease. TABLE 1 Modelresults for determining the tensile index at 1000 PFI revolutions CompR2Y Q2 RMSEE RMSEP NIR 2 0.76 0.33 2.11 1.43 FiberMaster 1 0.69 0.502.32 1.61 NIR + FiberMaster 2 0.85 0.56 1.69 0.98

[0062] The beating requirement to obtain a desired value of the tensileindex was modelled in the same way. FIGS. 8a-c and FIGS. 9a-c showcalibration models and the results for the off-line test set. The modelsfor the beating requirement are summarised in Table 2. The combinationof NIR and FiberMaster produced an improvement in the model results herealso. TABLE 2 Model results for determining the beating requirement forthe tensile index 70 Nm/g Comp R2Y Q2 RMSEE RMSEP NIR 2 0.79 0.41 98 96FiberMaster 1 0.70 0.54 112 82 NIR + FiberMaster 2 0.96 0.68 45 75

[0063] The invention is not restricted to the embodiments shown above,but can be varied within the scope of the following claims. It isperceived, for example, that the combination of NIR, fibre analysis andMVA can be carried out also on samples that are extracted at otherpositions in the pulp line, e.g. if the aim is primarily to control thepulp production process. The results of the analyses can also beutilized for feedback adjustment of the pulp production process. Theinvention can, in a broad aspect, also be used in the line for paper orpaperboard manufacture, it being possible for example for the measuringposition to be after beating or at beaters for different layers in apaperboard, with the possibility for example of determining a suitablewaste proportion. It is also perceived that other equipment can be used,for example another fibre meter that can provide the fibre propertydistributions, and that several computers can be used and linkedtogether for the different analyses and prediction.

1. Method for predicting properties of a product that consists ofcellulose-fibre-based pulp, paper or paperboard, a suspension ofcellulose fibre being analysed during manufacture of the product bymeans of spectroscopic measurements (13, 15) of a sample quantity ofsaid suspension in a selected spectrum in the wavelength range 200-25000nm, characterized in that each sample quantity, prior to said analysis,is diluted (3, 4), following which one partial flow (6) is dewatered anddried (8) and used for said spectroscopic measurements (13, 15), while asecond partial flow (5) of the diluted sample quantity is used foranalysis (7, 14) of physical fibre data, by means of image analysis,each sample quantity analysed generating at least 100 data points fromthe spectroscopic measurement distributed in the selected spectrum, andat least 50 data points with regard to said physical fibre data, in theform of one or more physical fibre property distributions with regard toat least one physical property in the group that consists of fibrelength, fibre width and fibre shape, which data points are combined inmultivariate data processing (16), for said prediction, on the basis ofcalibrations previously executed that were carried out on samples withknown product attributes.
 2. Method according to claim 1, characterizedin that said sample quantity suspension is extracted (1, 1′, 1 ^(n)) foranalysis at-line or on-line in a process line for said manufacture ofthe product, at least once every two hours, preferably at least once perhour, even more preferredly at least twice per hour and most preferredlyat least four times per hour.
 3. Method according to claim 2,characterized in that said sample quantity is extracted in a position ina process line for pulp production, which position is preferablydisposed prior to beating of the pulp, even more preferredly directlyprior to a drying stage for the pulp, which drying stage is disposeddirectly prior to the sale of dried pulp or directly prior to saidbeating of pulp.
 4. Method according to claim 3, characterized in thatsaid calibrations executed comprise calibrations against pulp samplesthat have been beaten with a number of different energy inputs, saidprediction giving a measure of the product attributes that it ispossible to achieve with one or more given energy inputs.
 5. Methodaccording to any of the above claims, characterized in that productattributes predicted by means of the method for an individual base paperbale or roll produced in pulp manufacture are compared with desiredproduct attributes for a number of different paper- orpaperboard-producing customers, this comparison being taken as a basisfor the choice of customer to whom the bale or roll is then delivered.6. Method according to claim 2, characterized in that said samplequantity is extracted at a position in a process line for paper orpaperboard production.
 7. Method according to any of the above claims,characterized in that said spectrum consists of the wavelength range400-2500 nm, preferably 780-2500 nm, in which spectrum at least 100 datapoints, preferably at least 300 data points, even more preferredly atleast 500 data points and most preferredly at least 800 data points aregenerated for each sample quantity, which data points consist of meanvalues of a number of sweeps in the spectrum, preferably at least 10sweeps.
 8. Method according to any of the above claims, characterized inthat said physical fibre data are obtained by means of a CCD camera, atleast 50 data points, preferably at least 100 data points and even morepreferredly at least 150 data points being generated for each samplequantity, which data points consist of distribution data with regard toat least two, preferably all physical attributes in the group consistingof fibre length, fibre width and fibre shape, and preferably also offibre weight per metre and fibre flexibility.
 9. Method according to anyof the above claims, characterized in that said dilution (3, 4) iscarried out during agitation to a concentration of less than 0.5%,preferably less than 0.1%, and that said dewatering and drying (8) iscarried out to at least 50% dry content, preferably at least 70% drycontent.
 10. Method according to claim 9, characterized in that saiddewatering and drying (8) is carried out by filtering (9) combined withforced air drying (10, 11, 12), preferably by means of direct contactwith compressed air.